The Misleading count: an identity-based intervention to counter partisan misinformation sharing

Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US (N = 1709) and UK (N = 804), we found that crowdsourcing accuracy judgements by adding a Misleading count (next to the Like count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared with the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly five times as effective as another popular misinformation intervention (i.e. the accuracy nudge reduced sharing misinformation by 5%). Extreme partisanship did not undermine the effectiveness of the intervention. Our results suggest that identity-based interventions based on the science of social norms can be more effective than identity-neutral alternatives to counter partisan misinformation in politically polarized contexts (e.g. the US). This article is part of the theme issue ‘Social norm change: drivers and consequences’.


Introduction
Online misinformation poses a substantial threat to democracy and public health.From the infodemic surrounding the COVID-19 pandemic [1,2] to the election fraud disinformation campaign that led to the 6 January assault on the US Capitol [3], misinformation appears to be a significant risk to public health and democratic institutions.On Twitter, falsehoods spread significantly farther, faster and deeper than true stories-and this was especially true for political and emotional stories [4].Online misinformation drives user engagement [4][5][6][7][8], capturing 2.3% of Web traffic and 14% of Facebook engagement according to recent estimates.As such, social media companies have few incentives to eliminate misinformation.Existing infrastructure for online content moderation has also proven unable to meet rapidly increasing demand: moderation is often outsourced to foreign workers who need to make split-second decisions on content that is highly dependent on local social and political contexts.Therefore it is critical to create systemic changes to social media infrastructure that can effectively reduce misinformation sharing in a way that is scalable, context-sensitive and effective among at-risk groups.In the current paper we develop and evaluate an identity-based intervention for reducing online misinformation sharing and compare it with popular approaches to reduce misinformation.
One of the most popular moderation-free approaches to combating misinformation is 'accuracy nudging': presenting users with visual or textual cues that remind them to be accurate.This approach is based on the idea that people largely share misinformation because they are inattentive or lack analytical thinking skills [9] and has received extensive empirical investigation [10].However, recent studies and a meta-analysis suggest that the effect of accuracy nudges may be relatively weak [11], especially among conservatives, Republicans, and far-right supporters [12,13].Similarly, a recent meta-analysis found that most strategies for debunking misinformation were not very effective overall (Cohen's d = 0.19), and were even less effective when the issue was politically polarized [14].As such, there is an urgent need to develop effective and scalable correction strategies for misinformation in the political domain that works across the political spectrum.
According to the Identity-Based Model of Political Belief [15], individuals are more likely to believe and share misinformation when their partisan motives outweigh accuracy concerns [16,17].This helps explain why partisan misinformation may be more difficult to debunk-especially in polarized contexts.For instance, we recently found that partisans who were highly devoted to a political party were more likely to spread misinformation than centrist votes and were unresponsive to fact-checking [13].The fact that interventions to counter misinformation based on analytical thinking are relatively ineffective for political extremists and right-wing users is practically important since these populations contribute the most to the spread of misinformation, at least in the US [18][19][20].It also underscores the need to incorporate social identity and group norms in the design of misinformation interventions.
Online misinformation is usually embedded in an interactive social environment (i.e.social media platforms) with visible social engagement metrics (e.g.number of Likes), which have been found to increase people's vulnerability to misinformation [21].However, this also offers great potential for interventions based on social psychology [22].For instance, actual reporting of fake news [23] and willingness to correct misinformation online [24] have been associated with social norms (i.e.beliefs on what others do or deem desirable, see [25]) about these behaviors.The effect of social norms may be more complex when it comes to misinformation about polarizing issues (e.g.election fraud allegations) since beliefs and behaviour are likely to be determined by the intergroup and intragroup dynamics of fellow partisans.In line with Social Identity Theory [26] and Self-Categorization Theory [27], opinions from the in-group tend to induce greater conformity than opinions from the out-group [28].Thus, in a polarized digital environment, people may be more likely to conform to in-group social norms than to social norms by general users.
Here, we propose that exposing individuals to normative accuracy judgements by their in-group (versus general others) may be helpful to counter partisan misinformation (e.g.misinformation that favours specific in-group partisan stances).Indeed, laypeople are relatively good at distinguishing low-quality news content [29], raising the possibility of crowdsourcing accuracy judgements.This norms-based approach could be particularly useful for misinformation on politically polarizing issues (e.g.attitudes towards immigration and universal healthcare, see [30,31]), which people are more likely to share than misinformation on non-polarizing issues (e.g.infrastructure, see [13]).Crowdsourcing only from the in-group may also contribute to correcting inaccurate perceptions of in-group norms over particular issues, which could help reduce misperceived polarization [32].Therefore, identity-based interventions that leverage normative cues to nudge people into being more accurate may be an effective and scalable approach to moderating online misinformation.

(a) Current research
In the present work, we tested the effect of normative accuracy judgements from the in-group to reduce sharing of partisan misinformation in three pre-registered online experiments with Democrats and Republicans in the US (N = 1709) and Labour and Conservative voters in the UK (N = 804).Although both contexts are politically polarized, a cross-country analysis found higher levels of affective polarization in the US compared with the UK [33].We asked participants how likely they would be to share a series of simulated social media posts composed by different in-group political leaders (e.g.Bernie Sanders for Democrats) that contained inaccurate information relevant to politically polarizing issues (e.g.immigration, homelessness).The intervention consisted of adding a Misleading count next to the Like count.Half of the participants were told the Misleading count reflected in-group norms, i.e. the number of fellow Democrats/Republicans who had tagged the post as misleading (identity-relevant condition).The other half were told the Misleading count reflected the norms of general users (identity-neutral condition).We compared this intervention with widely used interventions to counter misinformation, including (a) the official Twitter tag, a warning that precludes social media users from further sharing the posts ('This Tweet can't be replied to, shared or liked'), and (b) an accuracy nudge adapted from Pennycook et al. [34] ('To the best of your knowledge is the above statement accurate?').This allowed us to test the relative efficacy of different popular interventions against the identity-based intervention.
We predicted that people would report a lower likelihood of sharing social media posts in response to seeing the Misleading count compared with no count (H1).In Studies 2 and 3, we also expected the Misleading count to be more effective in reducing sharing whenever the count was 80 compared with 20% of the Like count (H2).Since politically polarizing issues typically involve absolutist stances over moral issues, which are particularly resistant to trade-offs and social influence [35], we expected a reduced effect of the Misleading count when the posts were relevant to polarizing (versus non-polarizing) issues in Experiment 1 (H3 in Experiment 1).We also expected the Misleading count to be similarly effective in reducing sharing of social media posts among Democrats and Republicans in the US, and among Labour and Conservative voters in the UK (H3 in Experiments 2 and 3).Finally, because people are responsive to in-group norms specifically [36], we expected the Misleading count to be more effective when it reflected in-group norms compared with general users' norms (H4).

(a) Participants
We recruited 401 Democratic and 402 Republican voters in the US for Experiment 1, 402 Labour voters and 402 Conservative voters in the UK for Experiment 2, and 453 Democratic and 452 Republican voters in the US for Experiment 3 by means of an online panel (Prolific).Inclusion criteria included being 18 or older and having voted for the relevant political party in the two previous presidential elections (see demographic information in electronic supplementary material, table S1 and power analysis in electronic supplementary material, methods, Participants).

(b) Material
The posts were designed to look like Tweets and contained inaccurate information about a series of political issues that we expected would be engaging to participants (see electronic supplementary material, tables S2-S4).The use of artificial rather than real misinformation allowed us to control both content and grammatical structure.The posts were tested for perceived accuracy, salience, familiarity and importance in a series of pilot studies with independent samples matched for country of residence and political affiliation (see electronic supplementary material, Methods, Materials and table S5).The pilot studies also confirmed that the information contained in the posts was neither too plausible nor too implausible to avoid ceiling and floor effects in participants' likelihood of sharing (see electronic supplementary material, table S5).Participants were exposed to information aligned with their political affiliation (e.g. in favour of universal healthcare for Democrats).
In Experiment 1, half of the social media posts included information about politically polarizing issues (e.g.immigration, universal healthcare), and the other half included non-polarizing issues (e.g.infrastructure).In Experiments 2 and 3, all the social media posts conveyed information about polarizing issues (e.g.immigration, universal healthcare).As expected, a larger proportion of participants held absolutist stances (resistant to economic trade-offs) over issues that we proposed as polarizing as compared with issues that we proposed as non-polarizing.Whether participants held absolutist stances over each issue was assessed in the same survey (see electronic supplementary material, Materials and table S6).

(c) Procedure
We launched surveys asking Democrats and Republicans in the US (Experiments 1 and 3) and Labour and Conservative voters in the UK (Experiment 2) to rate the likelihood of sharing a series of social media posts composed by different political leaders of the party they voted for in the last elections.We tested different variations of an identity-based intervention: we included a Misleading count next to the Like count, which we told participants reflected in-group norms, i.e. the number of fellow Democrats/Republicans who had tagged the post as misleading.The Misleading count was always lower than the Like count, as expected for highly partisan content.Social media posts with and without interventions were presented in a randomized order.
In Experiment 1 (N = 803), half of the social media posts contained a Misleading count that was 30% of the Like count (figure 1a) and the other half did not contain any intervention (control condition).
In Experiment 2 (N = 804), 25% of the social media posts contained a low Misleading count (20% of the Like count), 25% contained a high Misleading count (80% of the Like count), 25% contained an official Twitter Misleading tag (figure 1b), and 25% did not include any intervention (control condition).Because the official Twitter Misleading tag prevents participants from sharing the post, we asked participants how likely they would be to share the post through other means (e.g.taking a screenshot).Since sharing messages in this condition requires extra effort, the dependent variable (sharing intentions) is psychologically different in the official Twitter tag compared with the Misleading count conditions.The value of including an established intervention against misinformation (the official Twitter tag) lies in the comparison of final outcomes (whether the message is likely to be shared or not, independently of the means), which is relevant for potential implementations of the Misleading count.
In Experiment 3 (N = 905), we used the same design as in Experiment 2 but instead of the official Twitter Misleading tag, we compared the high and low Misleading count to an accuracy nudge ('To the best of your knowledge, is the above statement accurate?',adapted from Pennycook et al. [34]).Of note, the accuracy nudge was included directly on the social media posts (figure 1c) unlike in the original setting, where it was administered as a separate intervention at the beginning of the experiment [34].Thus, participants were exposed to the accuracy nudge more intensively than in the original setting.As such, the accuracy nudge effects might be stronger here than in the traditional implementation.
Across the three studies, all participants were exposed to all interventions and the control condition (within-subjects factor).All three studies included an additional between-subjects control condition so that half the sample was told that the Misleading count reflected general users' norms, i.e. the number of general users who had tagged the post as misleading (tagged by anyone) instead of only fellow Democrats/Republicans (tagged by in-group) (see electronic supplementary material, Methods for more details).

(a) Effect of the intervention
As predicted (H1), including a Misleading count next to the Like count (figure 1a) reduced participants' likelihood of sharing misinformation compared with the no intervention control condition across all three studies ( p < 0.001, Cohen's d = 0.20, see electronic supplementary material, table S7a, and figure 2).In z-score = −4.61,p < 0.001, d = 0.12, which prevents Twitter users from further sharing the post ('This Tweet can't be replied to, shared or liked', figure 1b).However, in Experiment 3, the high Misleading count worked better than the accuracy nudge ( figure 1c) in reducing participants' likelihood of sharing misinformation, M diff = −0.22,95% CI [−0.32, −0.12], z-score = −5.60,p < 0.001, d = 0.13.As such, the Misleading count appears to be a relatively effective strategy for reducing misinformation sharing.
In an explorative analysis looking at the dichotomized response variable (likely versus not likely to share) across the three experiments, the number of participants likely to share the social media posts (likelihood > 3) was reduced by around 25% in response to the Misleading count versus control in the in-group condition, M diff = −0.26,95% CI [−0.35, −0.16], t 95 = −6.85,p < 0.001, as compared with a 12% reduction in the general users' condition versus control, M diff = −0.12,95% CI [−0.22,−0.03], t 95 = −3.30,p = 0.007 (interaction effect: B = 0.13, 95% CI [0.03, 0.23], t 94 = 2.51, p = 0.014).The same model revealed a 34% reduction in the number of participants likely to share the social media To test the possibility of demand effects (i.e. to see if the 'study would become quite obvious' over time), we tested if the initial effects (first four trials) were different from the overall effects in an exploratory analysis.Presumably, the effect would change over time if demand effects increasingly came into play.However, the results of this additional analysis revealed that the early effects were nearly identical to the overall effects (see electronic supplementary material, table S8).Thus, potential demand effects do not appear to have changed our results in any measurable way.

(b) Extreme partisanship
Interventions to counter misinformation are often less effective when partisan incentives outweigh accuracy concernsfor instance, when misinformation is framed in terms of group-relevant politically polarizing issues, and for individuals who highly identify with the group [13].Thus, we tested if the Misleading count was also less effective in these conditions.In Experiment 1, we compared the effect of the intervention for misinformation relevant to politically polarizing issues compared with non-polarizing issues (as measured in our surveys, see the percentage of participants with absolutist stances over each issue in electronic supplementary material, table S6).Contrary to our expectations (H3 in Experiment 1), the Misleading count (versus control) was actually more effective for social media posts on polarizing issues (e.g.immigration, homelessness) than nonpolarizing issues (interaction effect: B = −0.13,95% CI [−0.25, −0.002], t 2406 = −2.00,p = 0.046, figure 2a,b).
Therefore, in Experiment 3, we made sure the stimuli for Republicans were matched with those presented to Democrats in perceived accuracy, attitude strength, familiarity and salience (see electronic supplementary material, table S5, and figure 3g).As a result, we found only a marginal difference in overall sharing between groups in Experiment 3, M diff = −0.18,95% CI [−0.38, 0.01], t 897 = −1.83,p = 0.067, d = 0.11, and if anything, the trend indicated higher sharing among Republicans (figure 3h).Despite having similarly appealing social media posts for Democrats and Republicans in Experiment 3, the effect of the intervention (versus control) was still larger in Democrats than Republicans (see electronic supplementary material, table S7c, and figure 2e), which is consistent with the accuracy nudge intervention [12].

(d) Group norms
In line with our hypothesis (H4), the Misleading count was more effective when it reflected in-group norms (i.e. the number of fellow Republicans/Democrats who had tagged the post as misleading), as compared with the norms of general users (see electronic supplementary material, table S7b, and figure 2b,d,f  However, the effect of the in-group was only observed in the US sample (Experiments 1 and 3) and was not found in the UK sample (Experiment 2, p > 0.194, see electronic supplementary material, table S7b), which was less polarized in royalsocietypublishing.org/journal/rstb Phil.Trans.R. Soc.B 379: 20230040 terms of political orientation (figure 3f ) than the US samples (figure 3c,i).Thus, incorporating the in-group dimension may be more effective in highly polarized US voters (see political orientation distribution in figure 3c,i) and less so among less polarized UK voters (see political orientation distribution in figure 3f ).

(e) Engagement
The social media posts used in Experiment 1 were designed to have higher social engagement metrics (from 782 to 16 900 Misleadings) than those in Experiments 2 and 3 (from 62 to 199 Misleadings, see all items in electronic supplementary material, tables S5-S7).In an explorative analysis, we found that participants were less likely to share misinformation the higher the absolute count of Misleadings was (Experiment 1: B = −0.00002,95% CI [−0.00003, −0.00001], t 9721 = −3.51,p < 0.001; Experiment 2: B = −0.0006,95% CI [−0.0008, −0.0004], t 2432 = −7.36,p < 0.001; Experiment 3: B = −0.0005,95% CI [−0.0007, −0.0004], t 3036 = −6.33,p < 0.001).Thus, more Misleadings were associated with less likelihood of sharing, and the cumulative effect of Misleadings in high engagement Tweets was enough to counteract the effect of the Misleading to Like ratio.If this ratio was all that mattered, the Misleading count in Experiment 1 (30% of the Like count) would be less effective in reducing sharing than the high Misleading count in Experiments 2 and 3 (80% of the Like count).However, in an explorative analysis combining the three data sets, we found the Misleading count in Experiment 1 to be similarly effective in reducing sharing compared with the high Misleading count in Experiments 2 and 3, that is, the interaction term between Experiment and

Discussion
We tested the effect of an identity-based misinformation intervention by adding a Misleading count next to the Like count on social media posts to reduce sharing of partisan misinformation in the US and the UK.Across three experiments, the number of people who reported they would be likely to share the social media posts dropped by 25% in response to the in-group Misleading count (versus control) as compared with 5% in response to an adapted version of the accuracy nudge.The Misleading count was also more effective when it reflected in-group norms (fellow Democrats/Republicans) compared with the norms of general users and when it was relatively high compared with the Like count.The effect of the in-group was not found in the UK sample, which was less politically polarized.Moreover, extreme partisanship, measured as both identity fusion with the political party and identity fusion with leaders, did not undermine the effectiveness of the intervention.These results provide initial evidence that identity-based interventions may be more effective than identity-neutral alternatives for addressing partisan misinformation in polarized contexts.
While completely preventing users from engaging with misinformation-the official Twitter tag condition-was most effective, this strategy relies heavily on moderators and is better suited for unequivocally false rather than misleading content.The Misleading count provides an additional and easily scalable layer against misinformation where social media users are able to regulate online content themselves.It had larger cumulative effects (25% fewer people sharing across experiments) than identity-neutral alternatives such as the accuracy nudge (5% fewer people sharing), and it was effective for extreme partisans.The Misleading count allows social media users to update their perceived social norms about a given message, which can lead people to conform to these judgements and make behavioural adjustments [25].In contrast to a Dislike count, the Misleading count provides normative information on the accuracy of the message.Thus, its function is to convey information about the quality of a message rather than the level of agreement with a given statement.
Using normative cues seems to be more effective in polarized contexts (e.g.US voters compared with UK voters) and for posts on polarizing issues (e.g.immigration) compared with non-polarizing issues (e.g.infrastructure).These findings suggest that polarized contexts offer either greater incentives to conform to in-group norms or greater disincentives not to conform to them-which is consistent with an identity-based approach to misinformation [13,15,16].As a result, people are more attuned to in-group (versus outgroup) norms in highly polarized contexts [38], especially when the issue at stake is fundamental to their status as group members.The effectiveness of in-group norms when group status is most salient (e.g. in polarized contexts and for posts on polarizing issues) also helps clarify why the Misleading count was effective even for extreme partisans who reported being fused with either political parties or leaders.Because fused individuals are more driven to match ingroup norms [39], the Misleading count and other normbased interventions appear to be particularly compelling for extreme partisans (see also [40]).
Our design offers novel data on the motivations underlying individuals' responses to the Misleading count.Partisans could use the in-group norms to identify the most effective posts to promote their views (competitive effectiveness hypothesis).This mechanism could trigger a backfire effect, increasing people's likelihood of sharing posts with a relatively low Misleading count compared with the Like count, over what would have been expected without any intervention (control condition).However, we did not find support for this hypothesis: even relatively few Misleadings (versus Likes) reduced participants' likelihood of sharing posts compared with the control condition.Conversely, partisans could use in-group norms as a trusted source to identify true information (civicmindedness hypothesis).This would involve reductions in sharing in response to any number of Misleadings irrespective of Likes.In line with this hypothesis, we found an effect of the absolute number of Misleadings in reducing participants' likelihood of sharing posts.Future studies should explore these differences in greater depth.
While the inclusion of a Misleading button that people can click on is straightforward to implement and scale, incorporating the in-group condition is more challenging.One option would be to replace the in-group (fellow Democrats/ Republicans) with 'people you follow' or by AI-generated user subgroups (e.g.'people like you').In this case, the Misleading count would only be altered by a particular subgroup, and each user would see a different Misleading count, preventing out-group members from abusing the Misleading button to 'attack' social media posts.This intervention relies on naturally occurring variation in how the in-group evaluates a particular social media post (some will Like it, others will tag it as misleading).While it is not clear how many in-group members will be willing to tag specific in-group misinformation as misleading, we found that just a few Misleading tags (e.g.20% of the Like count) are enough to have a deterrent effect.Future research should assess if 20% is a realistic assumption and see if people are willing to tag in-group content as misleading.Prior research suggests that crowdsourcing accuracy judgements may be feasible and effective [29].
Similarly to other interventions such as the accuracy nudge [12,13], the Misleading count is less effective for conservatives than liberals.This partisan difference could be related to perceptions of the existing norms within these political groups, labelling interventions as punitive and biased [41], or psychological differences in need of closure [42] and accuracy motivation [18].In the case of the Misleading count, this limitation can be partially compensated with a higher total Misleading count (e.g. in Experiment 1).Thus, betweengroup differences in the effect of the intervention notably decrease for high-engagement Tweets with higher total Misleading counts.
The main limitation of the present study is that it is a series of controlled experiments with carefully curated social media posts.Although intentions to share are highly correlated with real-world sharing [43], more ecologically valid approaches are necessary to determine its effectiveness in a real-world social media setting.This is especially important for royalsocietypublishing.org/journal/rstb Phil.Trans.R. Soc.B 379: 20230040 intervention studies that report small to medium effect sizes in samples of panel respondents such as the present study.In a more realistic setting, people would be exposed to a collection of both partisan and nonpartisan social media posts with accurate and inaccurate information and would be able to actually share the posts with others.Related to this, although our likelihood of sharing measure is widely employed to assess intentions to share social media posts (e.g.[29]), it could be that it overestimates people's actual likelihood to share posts in the real world.Future research could thus test the proposed intervention within a social media simulation or in field studies.
Moreover, we did not measure how perceived norms about the accuracy of each message changed before and after the intervention.Thus, we cannot directly test whether shifts in perceived social norms mediate the effect of the intervention on sharing intentions.Finally, the current research includes liberal and conservative voters in the US and the UK, limiting the generalizability of the findings to these populations.

Conclusion
Identity-based interventions that incorporate normative cues appear to be more effective than identity-neutral interventions to counter partisan misinformation among individuals in politically polarized contexts (e.g.US voters).Particularly, pairing partisan misinformation with in-group accuracy judgements reduced misinformation sharing among partisans in the US and the UK.This strategy was effective even for extreme partisans who identified highly with their political leader.Thus, allowing social media users to publicly tag posts as misleading could contribute to stopping the spread of misinformation.
Ethics.This research was approved by the Ethics Committee on Human and Animal Experimentation at the Universitat Autònoma de Barcelona according to the Declaration of Helsinki guidelines (ref.5820).

Figure 1 .
Figure 1.Employed interventions.Examples of the employed interventions including (a) the Misleading count condition (30% of the Like count) used in Experiment 1, (b) the official Twitter Misleading tag used in Experiment 2, and (c) the accuracy nudge used in Experiment 3. royalsocietypublishing.org/journal/rstb Phil.Trans.R. Soc.B 379: 20230040

Figure 2 .
Figure 2. Effect of the intervention across experiments.(a,c,e) Likelihood of sharing social media posts on polarizing issues (Experiments 1-3) and non-polarizing issues (Experiment 1) in response to the Misleading count (Experiments1-3), the official Twitter tag (Experiment 2) and the accuracy nudge (Experiment 3) compared with control by group and condition (tagged by in-group' and 'tagged by anyone).In Experiment 1, the Misleading count was always 30% of the Like count.In Experiments 2 and 3, the Misleading count was presented in two conditions: high count (80% of the Like count) and low count (20% of the Like count).The absolute Misleading count was two orders of magnitude higher in Experiment 1 as compared with Experiments 2 and 3 (e.g. 10 000 versus 100).Error bars represent 95% confidence intervals.(b,d,f ) Coefficient estimates of the contrast between each intervention compared wiith control for social media posts relevant to polarizing (Experiments 1-3) and non-polarizing issues (Experiment 1) by condition (tagged by in-group and tagged by anyone).Error bars represent 95% confidence intervals.Asterisks represent levels of significance.*p-value <0.05, **p-value <0.01, ***p-value <0.001.

Figure 3 .
Figure 3. Perceived accuracy, attitude strength, and likelihood of sharing social media posts across experiments.(a,d,g) Perceived accuracy, attitude strength (certainty, extremity, and importance), familiarity, and salience of the used social media posts by group (Exp.1-3) and type of issue ( polarizing and non-polarizing) (Exp. 1) as tested in pilot studies (Exp 1: N = 370; Exp 2: N = 80; Exp 3: N = 234).(b,e,g) Likelihood of sharing social media posts on polarizing issues (Exp.1-3) and non-polarizing issues (Exp.1) as a function of political affiliation.(c,f,i) Extreme political orientation was associated with an increased likelihood of sharing social media posts in the control condition (no interventions) across samples and political groups (liberals in blue and conservatives in red).Notably, U.S. samples in Exp. 1 and 3 were more polarized in terms of political orientation compared to the UK sample in Exp. 2.